Complementary Weighted Iterative Fusion Algorithm for Multi Source Sensor Data Based on Kalman Filtering
Because multi-source sensors are greatly affected by their own data differences in the data fusion process,resulting in low accu-racy of the final fusion results.Therefore,based on Kalman filtering algorithm,a complementary weighted iterative fusion algorithm is pro-posed for multi-source sensor data.The multi-source sensor observation model is established to find the optimal weighting coefficient in the data fusion process.Kalman filtering algorithm is introduced into multi-source sensor combination system,combined with complementary weighted iterative fusion algorithm,prediction equation,state equation,filter complementary factor and estimation mean square error equa-tion are established to achieve multi-source sensor data fusion.The experimental results show that the proposed algorithm can accurately find the optimal weighting coefficient,and the observation error is always below 0.6 m,which can achieve accurate data fusion.
multi source sensordata complementary weighted iterative fusionKalman filtering algorithmequation of stateoptimal weighting coefficient
万方数据
维普
